RUNX1 promotes proliferation and migration in non-small cell lung cancer cell lines via the mTOR pathway.
Huan MaSiyu JiangYinan YuanJi LiYizhuo LiYanping LvTengjiao DuJingqian GuanXizi JiangLei TianQianqian ZhengLianhe YangQingchang LiPublished in: FASEB journal : official publication of the Federation of American Societies for Experimental Biology (2023)
RUNX1, a member of the RUNX family of metazoan transcription factors, participates in the regulation of differentiation, proliferation, and other processes involved in growth and development. It also functions in the occurrence and development of tumors. However, the role and mechanism of action of RUNX1 in non-small cell lung cancer (NSCLC) are not yet clear. We used a bioinformatics approach as well as in vitro and in vivo assays to evaluate the role of RUNX1 in NSCLC as the molecular mechanisms underlying its effects. Using the TCGA, GEO, GEPIA (Gene Expression Profiling Interactive Analysis), and Kaplan-Meier databases, we screened the differentially expressed genes (DEGs) and found that RUNX1 was highly expressed in lung cancer and was associated with a poor prognosis. Immunohistochemical staining based on tissue chips from 110 samples showed that the expression of RUNX1 in lung cancer tissues was higher than that in adjacent normal tissues and was positively correlated with lymph node metastasis and TNM staging. In vitro experiments, we found that RUNX1 overexpression promoted cell proliferation and migration functions and affected downstream functional proteins by regulating the activity of the mTOR pathway, as confirmed by an analysis using the mTOR pathway inhibitor rapamycin. In addition, RUNX1 affected PD-L1 expression via the mTOR pathway. These results indicate that RUNX1 is a potential therapeutic target for NSCLC.
Keyphrases
- transcription factor
- poor prognosis
- genome wide identification
- small cell lung cancer
- lymph node metastasis
- cell proliferation
- long non coding rna
- genome wide
- gene expression
- dna binding
- squamous cell carcinoma
- stem cells
- signaling pathway
- risk assessment
- machine learning
- bone marrow
- epidermal growth factor receptor
- big data
- binding protein